Configural frequency trees. (10th October 2022)

• Record Type: Journal Article
• Authors: Wiedermann, Wolfgang; Herman, Keith C.; Reinke, Wendy; von Eye, Alexander
• Is Part Of: Development and psychopathology. Volume 34:Number 4(2022)
• Page Start: 1585
• Page End: 1603
• Publication Date: 2022-10-10
• Subjects: configural frequency analysis -- model-based recursive partitioning -- moderation -- person-oriented research -- regression trees; Child psychopathology -- Periodicals; Developmental psychology -- Periodicals; Psychology, Pathological -- Periodicals; 618.9289
• DOI: 10.1017/S0954579421000018
• Languages: English
• ISSNs: 0954-5794

Abstract

Abstract: Although variable-oriented analyses are dominant in developmental psychopathology, researchers have championed a person-oriented approach that focuses on the individual as a totality. This view has methodological implications and various person-oriented methods have been developed to test person-oriented hypotheses. Configural frequency analysis (CFA) has been identified as a prime method for a person-oriented analysis of categorical data. CFA searches for configurations in cross-classifications and asks whether the number of observed cases is larger (CFA type) or smaller (CFA antitype) than expected under a probability model. The present study introduces a combination of CFA and model-based recursive partitioning (MOB) to test for type/antitype heterogeneity in the population. MOB CFA is well suited to detect complex moderation processes and can distinguish between subpopulation and population types/antitypes. Model specifications are discussed for first-order CFA and prediction CFA. Results from two simulation studies suggest that MOB CFA is able to detect moderation processes with high accuracy. Two empirical examples are given from school mental health research for illustrative purposes. The first example evaluates heterogeneity in student behavior types/antitypes, the second example focuses on the effect of a teacher classroom management intervention on student behavior. An implementation of the approach is provided in R.